GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing

A DeepSWE v1.1 comparison across 113 software-engineering tasks and four trials per model found GLM-5.3 and Claude Fable 5 statistically close on first-attempt accuracy, with Fable at 69.7% pass@1 and GLM at 69.0%, but GLM led on repeated attempts, reaching 81.1% versus 77.1% at pass@2 and 87.6% versus 84.1% at pass@4. GLM’s reported rollout cost was $3.99 compared with Fable’s $21.63, producing an estimated 17 versus 3 solved tasks per $100, while average completion times were similar at roughly 35 minutes. GLM showed broader task coverage and strengths in JavaScript, TypeScript, concurrency, query/configuration work, runtime internals, and program analysis, whereas Fable performed better in Rust, Python, data modeling, serialization, build and operations, and protocol-conformance tasks. Both models had similarly low regression-related failure rates and a high per-task outcome correlation of 0.65, meaning that using both together added limited coverage; the analysis therefore recommends GLM-5.3 as a lower-cost default, with Fable reserved for Rust-heavy or serialization-critical work, while noting scoring choices, infrastructure errors, and incomplete public trajectory data as caveats.

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